Searching for subsurface mineral and hydrocarbon deposits comprises data acquisition, analysis, and interpretation procedures. Data acquisition involves energy sources generating signals propagating into the earth and reflecting from subsurface geologic structures. The signals received are recorded by receivers on or near the surface of the earth. The received signals are stored as time series (seismic traces) that consist of amplitudes of acoustic energy which vary as a function of time, receiver position, and source position and, most importantly, vary as a function of the physical properties of the structures from which the signals reflect. The data are generally processed to create volumes of acoustic images from which data analysts (interpreters) create maps and images of the subsurface of the earth.
Data processing involves procedures that vary depending on the nature of the data acquired and the geological structure being investigated. A typical seismic data processing effort produces images of geologic structure. The final product of data processing sequence depends on the accuracy of these analysis procedures.
Processed seismic data are interpreted to make maps of subsurface geologic structure to aid decisions for subsurface mineral exploration. The interpreter's task is to assess the likelihood that subsurface hydrocarbon deposits are present. The assessment will lead to an understanding of the regional subsurface geology, important main structural features, faults, synclines and anticlines. Maps and models of the subsurface, both in 2D and 3D representations are developed from the seismic data interpretations. As is well known in the art, the quality and accuracy of the seismic data processing has a significant impact on the accuracy and usefulness of the interpreted data.
High quality data processing greatly simplifies data interpretation, since resources can be focused on the geologic structure since subsurface imaging can be made less ambiguous. Unfortunately, three dimensional geophysical data processing and/or modeling frequently require large computation expenses, and practitioners are forced to simplify the data processing effort as much as possible to reduce analysis time and cost.
The sheer volume of data impacts data processing considerations. Seismic survey data sets can involve hundreds of thousands of source locations, with each source location associated with many hundreds more receiver locations. Each input/output data transfer demand burdens resources independent of the computation burden.
There have been several different approaches to manage these computational resource burdens. These approaches relate to the manner in which the data acquisition exercise is designed and carried out, as well as to assumptions made during data processing. The use of available a priori geologic and geophysical information can facilitate the minimization of the seismic data acquisition effort. Such a minimization of resources reduces the amount of data that is acquired by reducing the acquisition effort.
Minimization of the computational effort is often implemented during data processing. Compromises often required during data acquisition and processing can result in ambiguous and/or inaccurate subsurface images. Because little is generally known of the geologic structure being investigated, the interpreter will not know the extent the images are erroneous.
It is not uncommon for significant computer resources to be involved when large or complex data volumes are processed, often involving weeks or months of actual computer processing time. The recent availability of massively parallel processor computers offers a significant opportunity to reduce overall processing times. Massively parallel processors (MPPs) can have multiple central processing units (CPUs) which can perform simultaneous computations. By efficient use of these CPUs, projects that took weeks or months of resource time previously can be reduced to a few days or a few hours. These advantages can be enhanced further when efficient algorithms are included in the MPP software.
Computational algorithms have previously been written for prior seismic analysis routines using single or just a few processors, usually using sequential computing. Sequential computing performs single procedures at any given time. Options for obtaining enhanced performance are limited when few processors are available.
MPP computing machines offer an obvious computation advantages. The total time required to process a dataset can be reduced by dividing the work to be done among the various CPUs or CPU clusters in manner such that each CPU performs useful work while other CPUs also work in parallel.
Seismic data consists generally of a large number of individual traces, each recorded somewhat independently of the other traces. Logically enough, sequential computing methods that require the processing focus to be placed on a single calculation at a time adapt well to analysis of these independent traces using parallel processing. This is true even though computational bottlenecks may exist. For example, portions of the processing sequence may require relatively more computation time than other portions, must be completed before other calculations may proceed, or may rely on input data other than seismic data, for example traveltimes. Even though parallel processing significantly decreases total processing times, efficient processing algorithms can also contribute significant time reductions.
For large datasets frequency domain methods offer significant computation cost savings. An option for overcoming increased computational loads is to employ efficient frequency domain migration algorithms.
It would therefore be desirable to have a system and method that is able to provide a migration in the frequency-wavenumber domain on parallel computer in a cost-effective manner. The present invention satisfies this need.